Simulations of biomass dynamics in community food webs

Delmas, Eva; Brose, Ulrich; Gravel, Dominique; Stouffer, Daniel B.; Poisot, Timothée

doi:10.1111/2041-210x.12713

Cited by 29 publications

(42 citation statements)

References 22 publications

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“…Another important factor identified in body size changes on islands are marine subsidies, which increase body size (Lomolino et al, ). Further incorporating marine subsidies through food web models that include biomass dynamics (e.g., Delmas, Brose, Gravel, Stouffer, & Poisot, ) may help to understand and predict the strength of the island rule.…”

Section: Island Rulementioning

confidence: 99%

Ecogeographical rules and the macroecology of food webs

Baiser

Gravel

Cirtwill

et al. 2019

Global Ecol Biogeogr

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Aim How do factors such as space, time, climate and other ecological drivers influence food web structure and dynamics? Collections of well‐studied food webs and replicate food webs from the same system that span biogeographical and ecological gradients now enable detailed, quantitative investigation of such questions and help integrate food web ecology and macroecology. Here, we integrate macroecology and food web ecology by focusing on how ecogeographical rules [the latitudinal diversity gradient (LDG), Bergmann's rule, the island rule and Rapoport's rule] are associated with the architecture of food webs. Location Global. Time period Current. Major taxa studied All taxa. Methods We discuss the implications of each ecogeographical rule for food webs, present predictions for how food web structure will vary with each rule, assess empirical support where available, and discuss how food webs may influence ecogeographical rules. Finally, we recommend systems and approaches for further advancing this research agenda. Results We derived testable predictions for some ecogeographical rules (e.g. LDG, Rapoport's rule), while for others (e.g., Bergmann's and island rules) it is less clear how we would expect food webs to change over macroecological scales. Based on the LDG, we found weak support for both positive and negative relationships between food chain length and latitude and for increased generality and linkage density at higher latitudes. Based on Rapoport's rule, we found support for the prediction that species turnover in food webs is inversely related to latitude. Main conclusions The macroecology of food webs goes beyond traditional approaches to biodiversity at macroecological scales by focusing on trophic interactions among species. The collection of food web data for different types of ecosystems across biogeographical gradients is key to advance this research agenda. Further, considering food web interactions as a selection pressure that drives or disrupts ecogeographical rules has the potential to address both mechanisms of and deviations from these macroecological relationships. For these reasons, further integration of macroecology and food webs will help ecologists better understand the assembly, maintenance and change of ecosystems across space and time.

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Section: Island Rulementioning

confidence: 99%

Ecogeographical rules and the macroecology of food webs

Baiser

Gravel

Cirtwill

et al. 2019

Global Ecol Biogeogr

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“…Describing the change in population density over time is classically done by using ordinary differential equations (ODEs); this approach is applied to model a wide range of ecological systems, from single populations to multitrophic communities (e.g., Delmas, Brose, Gravel, Stouffer, & Poisot, ; Rall et al., ; Rosenzweig & Mac Arthur, ; Verhulst, ). To correctly describe prey depletion during the course of a functional response experiment, we can set up an equation using the generalized Holling functional response (Equation ):

\frac{d N}{d t} = - \frac{b N^{1 + q}}{1 + b h N^{1 + q}} P .

…”

Section: Introductionmentioning

confidence: 99%

Fitting functional responses: Direct parameter estimation by simulating differential equations

Rosenbaum

Rall

2018

Methods Ecol Evol

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The feeding functional response is one of the most widespread mathematical frameworks in ecology, marine biology, freshwater biology, microbiology, and related scientific fields describing the resource‐dependent uptake of a consumer. Since the exact knowledge of its parameters is crucial to predict, for example, the efficiency of biocontrol agents, population dynamics, food web structure, and subsequently biodiversity, a trustworthy parameter estimation method is highly important for scientists using this framework. Classical approaches for estimating functional response parameters lack flexibility and often only provide approximations of the correct parameters. Here, we combined ordinary differential equation (ODE) models that were numerically solved using computer simulations with an iterative maximum likelihood fitting approach. We compared our method to classical approaches of fitting functional responses using data both with and without additional resource growth and mortality. We found that for classical functional response models, such as the frequently used type II and type III functional responses, the established fitting methods are reliable. However, by using more complex and flexible functional responses, our new method outperforms the traditional methods. Additionally, our method allows the incorporation of side effects such as resource growth and background mortality. Our method will enable researchers from different scientific fields who are measuring functional responses to calculate more accurate parameter estimates. These estimates will enable community ecologists to parameterize their models more precisely, thus allowing a deeper understanding of complex ecological systems, and will increase the quality of ecological prediction models.

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“…Describing the change of the population density over time is classically done by using ordinary differential equations (ODE); this approach is applied to model a wide range of ecological systems, from single populations up to whole multi-trophic communities (e.g. Verhulst, 1838; Rosenzweig & Mac Arthur, 1963; Rall, Guill & Brose, 2008; Delmas et al , 2016). To correctly describe the prey depletion during the course of a functional response experiment, we can set up such an equation using the generalized Holling functional response (eqn.…”

Section: Introductionmentioning

confidence: 99%

Fitting functional responses: Direct parameter estimation by simulating differential equations

Rosenbaum

Rall

2017

Preprint

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Summary1. The feeding functional response is one of the most widespread mathematical frameworks in Ecology, Marine Biology, Freshwater Biology, Microbiology and related scientific fields describing the resource-dependent uptake of a consumer. Since the exact knowledge of its parameters is crucial in order to predict, for example, the efficiency of biocontrol agents, population dynamics, food web structure and subsequently biodiversity, a trustful parameter estimation is of utmost importance for 1 . CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/201632 doi: bioRxiv preprint first posted online Oct. 13, 2017; scientists using this framework. Classical approaches for estimating functional response parameters lack flexibility and can often only serve as approximation for a correct parameter estimation. Moreover, they do not allow to incorporate side effects such as resource growth or background mortality. Both call for a new method to be established solving these problems.2. Here, we combined ordinary differential equation models (ODE models), that were numerically solved using computer simulations, with an iterative maximum likelihood fitting approach. We compared our method to classical approaches of fitting functional responses, using data both with and without additional resource growth and mortality.3. We found that for classical functional response models, like the often used type II and type III functional response, the established fitting methods are reliable. 4. Our method will enable researchers from different scientific fields that are measuring functional responses to estimate parameters correctly. These estimates will enable community ecologists to parameterize their models more precisely, allowing for a deeper understanding of complex ecological systems, and will increase the quality of ecological prediction models.

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Simulations of biomass dynamics in community food webs

Cited by 29 publications

References 22 publications

Ecogeographical rules and the macroecology of food webs

Ecogeographical rules and the macroecology of food webs

Fitting functional responses: Direct parameter estimation by simulating differential equations

Fitting functional responses: Direct parameter estimation by simulating differential equations

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